@InBook{	  hirst30,
  author	= {Graeme Hirst},
  chapter	= {Resolving lexical ambiguity computationally with spreading
		  activation and Polaroid Words},
  title		= {Lexical ambiguity resolution},
  editor	= {Steven Small and Garrison Cottrell and Michael Tanenhaus},
  publisher	= {Los Altos, CA: Morgan Kaufmann},
  year		= {1988},
  pages		= {73--107},
  note		= {2003 epilogue to this paper:&nbsp; <a
		  href=http://ftp.cs.toronto.edu/pub/gh/Hirst-88-Epilogue-2003.pdf>PDF</a>}
		  ,
  abstract	= {<p> Any computer system for understanding natural language
		  input (even in relatively weak senses of the word
		  <i>understanding</i>) needs to be able to resolve lexical
		  ambiguities. In this paper, I describe the lexical
		  ambiguity resolution component of one such system. </p><p>
		  The basic strategy used for disambiguation is ``do it the
		  way people do.'' While cognitive modeling is not the
		  primary goal of this work, it is often a good strategy in
		  artificial intelligence to consider cognitive modeling
		  anyway; finding out how people do something and trying to
		  copy them is a good way to get a program to do the same
		  thing. In developing the system below, I was strongly
		  influenced by psycholinguistic research on lexical access
		  and negative priming---in particular by the results of
		  Swinney; Seidenberg, Tanenhaus, Leiman, and Bienkowski; and
		  Reder. I will discuss the degree to which the system is a
		  model of ambiguity resolution in people.</p>},
  download	= {http://ftp.cs.toronto.edu/pub/gh/Hirst-88.pdf}
}

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